Optimization of permanent breast seed implant dosimetry incorporating tissue heterogeneity
Bibliographic record
Abstract
Seed brachytherapy is currently used for adjuvant radiotherapy of early stage prostate and breast cancer patients. The current standard for calculation of dose around brachytherapy sources is based on the AAPM TG43 formalism, which generates the dose in homogeneous water medium. Recently, AAPM task group no. 186 (TG186) emphasized the importance of accounting for heterogeneities. In this work we introduce an analytical dose calculation algorithm in heterogeneous media using CT images. The advantages over other methods are computational efficiency and the ease of integration into clinical use. An Inhomogeneity Correction Factor (ICF) is introduced as the ratio of absorbed dose in tissue to that in water medium. ICF is a function of tissue properties and independent of the source structure. The ICF is extracted using CT images and the absorbed dose in tissue can then be calculated by multiplying the dose as calculated by the TG43 formalism times ICF. To evaluate the methodology, we compared our results with Monte Carlo simulations as well as experiments in phantoms with known density and atomic compositions. The dose distributions obtained through applying ICF to TG43 protocol agreed very well with those of Monte Carlo simulations and experiments in all phantoms. In all cases, the mean relative error was reduced by at least a factor of two when ICF correction factor was applied to the TG43 protocol. In conclusion we have developed a new analytical dose calculation method, which enables personalized dose calculations in heterogeneous media using CT images. The methodology offers several advantages including the use of standard TG43 formalism, fast calculation time and extraction of the ICF parameters directly from Hounsfield Units. The methodology was implemented into our clinical treatment planning system where a cohort of 140 patients were processed to study the clinical benefits of a heterogeneity corrected dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".